Project Summary American Indians (AIs) suffer disproportionately from type 2 diabetes (T2D). Discovery of novel mechanistic biomarkers is the key to identify at-risk individuals and to develop effective preventive strategies tailored to this high risk population. In response to PA-12-165, this project leverages the wealth of unique resources collected by the Strong Heart Study (SHS), the largest longitudinal cohort study of American Indians followed over 25 years, to identify sensitive and specific metabolic markers that are predictive of T2D risk at preclinical stages above and over standard clinical factors including obesity, fasting glucose and insulin resistance. Metabolomics is an emerging technology that can simultaneously identify and accurately quantify hundreds to thousands of metabolites in biofluids. Several metabolites, such as BCAAs, acylcarnitines, and lipids, have been associated with T2D, but these results were largely derived from cross-sectional studies in almost exclusively European Caucasians. However, given the genetic regulation of metabolism, metabolites identified in Caucasians may not be generalized to AIs who may have a different genetic make-up. In addition, cross- sectional analysis cannot capture the dynamic trajectory of metabolic changes over time. Moreover, most existing studies measured a list of pre-selected metabolites on a single platform, but given the complexity of the human metabolome and the substantial diversity of metabolites, no single analytical platform can detect all metabolites in a biological sample. We hypothesize that longitudinal changes in plasma metabolites predict T2D risk independent of fasting glucose, insulin resistance (IR) and obesity, and that metabolic profiles of T2D in AIs are similar to, but distinct from, those in Caucasians. Our goal here is to identify novel and sensitive T2D predictors that are specific to AIs beyond classical T2D indicators. To achieve this, we will repeatedly measure concentrations of over 500 metabolites, including BCAAs, carbohydrates, hydroxyl acids, lipids, as well as gut microbial-derived metabolites, in fasting plasma (~5 yr apart) from normoglycemic SHS participants followed >15 years. Putative metabolites will be replicated in an independent longitudinal sample of AIs followed for 10 years. To increase coverage, we will quantify metabolites concentrations on three complementary platforms. Each assay will be performed as a dual 'targeted' and 'untargeted' analyses to provide both hypothesis-driven quantitative data and discovery-driven semi-quantitative data of unidentified metabolites. Unknown compounds will be identified by well-established workflows. Multivariate analyses will be conducted to identify novel T2D predictors above and over standard clinical factors. Our multidisciplinary team consists of experts with complementary expertise in diabetes epidemiology, metabolomics, analytical chemistry, statistics and bioinformatics. Findings of this study will greatly advance our understanding of T2D pathology, and hold promise for reducing or eliminating T2D disparity in AIs, an ethnically important but traditionally understudied minority group suffering from alarmingly high rates of T2D and obesity.